Papers by Emily Cheng

3 papers
Geometric Signatures of Compositionality Across a Language Model’s Lifetime (2025.acl-long)

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Challenge: linguistic compositionality allows atoms to locally combine to create global meaning . a rich array of meanings at the level of a phrase may be explained by simple rules of composition.
Approach: They propose to relate the degree of compositionality in a dataset to the intrinsic dimension of its representations under an LM, a measure of feature complexity.
Outcome: The proposed model is based on a geometric view of the compositionality of a dataset and the intrinsic dimension of its representations under an LM.
On the Correspondence between Compositionality and Imitation in Emergent Neural Communication (2023.findings-acl)

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Challenge: a study examining compositionality and imitation learning in a Lewis game demonstrates that it is difficult to imitate compositional languages.
Approach: They explore the link between compositionality and imitation in a Lewis game . they show that the learning algorithm used to imitate is crucial .
Outcome: The proposed model improves compositionality and imitation in a Lewis game . the study shows that compositional languages are easier to imitate .
Bridging Information-Theoretic and Geometric Compression in Language Models (2023.emnlp-main)

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Challenge: Current language models (LMs) encode training data into finitely many variables that allow generalization to infinitely many grammatical utterances.
Approach: They propose to analyze compression in language models from geometric and information-theoretic perspectives.
Outcome: The proposed model can model human language in a relatively small dimension.

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